Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Sunday 14 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 142,487 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 142,477 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 30
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 16
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 15
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 4
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 4
## 102 2020-06-10 East of England 6
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 3
## 105 2020-03-01 London 0
## 106 2020-03-02 London 0
## 107 2020-03-03 London 0
## 108 2020-03-04 London 0
## 109 2020-03-05 London 0
## 110 2020-03-06 London 1
## 111 2020-03-07 London 1
## 112 2020-03-08 London 0
## 113 2020-03-09 London 1
## 114 2020-03-10 London 0
## 115 2020-03-11 London 7
## 116 2020-03-12 London 6
## 117 2020-03-13 London 10
## 118 2020-03-14 London 14
## 119 2020-03-15 London 10
## 120 2020-03-16 London 18
## 121 2020-03-17 London 25
## 122 2020-03-18 London 31
## 123 2020-03-19 London 25
## 124 2020-03-20 London 44
## 125 2020-03-21 London 50
## 126 2020-03-22 London 54
## 127 2020-03-23 London 64
## 128 2020-03-24 London 87
## 129 2020-03-25 London 113
## 130 2020-03-26 London 130
## 131 2020-03-27 London 130
## 132 2020-03-28 London 122
## 133 2020-03-29 London 147
## 134 2020-03-30 London 150
## 135 2020-03-31 London 181
## 136 2020-04-01 London 202
## 137 2020-04-02 London 190
## 138 2020-04-03 London 196
## 139 2020-04-04 London 230
## 140 2020-04-05 London 195
## 141 2020-04-06 London 198
## 142 2020-04-07 London 219
## 143 2020-04-08 London 238
## 144 2020-04-09 London 206
## 145 2020-04-10 London 170
## 146 2020-04-11 London 177
## 147 2020-04-12 London 158
## 148 2020-04-13 London 166
## 149 2020-04-14 London 144
## 150 2020-04-15 London 142
## 151 2020-04-16 London 139
## 152 2020-04-17 London 100
## 153 2020-04-18 London 101
## 154 2020-04-19 London 103
## 155 2020-04-20 London 95
## 156 2020-04-21 London 95
## 157 2020-04-22 London 108
## 158 2020-04-23 London 77
## 159 2020-04-24 London 71
## 160 2020-04-25 London 58
## 161 2020-04-26 London 53
## 162 2020-04-27 London 51
## 163 2020-04-28 London 43
## 164 2020-04-29 London 44
## 165 2020-04-30 London 40
## 166 2020-05-01 London 41
## 167 2020-05-02 London 40
## 168 2020-05-03 London 36
## 169 2020-05-04 London 30
## 170 2020-05-05 London 25
## 171 2020-05-06 London 37
## 172 2020-05-07 London 37
## 173 2020-05-08 London 29
## 174 2020-05-09 London 23
## 175 2020-05-10 London 26
## 176 2020-05-11 London 18
## 177 2020-05-12 London 18
## 178 2020-05-13 London 16
## 179 2020-05-14 London 20
## 180 2020-05-15 London 18
## 181 2020-05-16 London 14
## 182 2020-05-17 London 15
## 183 2020-05-18 London 9
## 184 2020-05-19 London 13
## 185 2020-05-20 London 19
## 186 2020-05-21 London 12
## 187 2020-05-22 London 10
## 188 2020-05-23 London 6
## 189 2020-05-24 London 7
## 190 2020-05-25 London 9
## 191 2020-05-26 London 12
## 192 2020-05-27 London 7
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## 194 2020-05-29 London 7
## 195 2020-05-30 London 12
## 196 2020-05-31 London 6
## 197 2020-06-01 London 10
## 198 2020-06-02 London 7
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## 201 2020-06-05 London 3
## 202 2020-06-06 London 0
## 203 2020-06-07 London 4
## 204 2020-06-08 London 5
## 205 2020-06-09 London 2
## 206 2020-06-10 London 6
## 207 2020-06-11 London 3
## 208 2020-06-12 London 0
## 209 2020-03-01 Midlands 0
## 210 2020-03-02 Midlands 0
## 211 2020-03-03 Midlands 1
## 212 2020-03-04 Midlands 0
## 213 2020-03-05 Midlands 0
## 214 2020-03-06 Midlands 0
## 215 2020-03-07 Midlands 0
## 216 2020-03-08 Midlands 3
## 217 2020-03-09 Midlands 1
## 218 2020-03-10 Midlands 0
## 219 2020-03-11 Midlands 2
## 220 2020-03-12 Midlands 6
## 221 2020-03-13 Midlands 5
## 222 2020-03-14 Midlands 4
## 223 2020-03-15 Midlands 5
## 224 2020-03-16 Midlands 11
## 225 2020-03-17 Midlands 8
## 226 2020-03-18 Midlands 13
## 227 2020-03-19 Midlands 8
## 228 2020-03-20 Midlands 28
## 229 2020-03-21 Midlands 13
## 230 2020-03-22 Midlands 31
## 231 2020-03-23 Midlands 33
## 232 2020-03-24 Midlands 41
## 233 2020-03-25 Midlands 48
## 234 2020-03-26 Midlands 64
## 235 2020-03-27 Midlands 72
## 236 2020-03-28 Midlands 89
## 237 2020-03-29 Midlands 92
## 238 2020-03-30 Midlands 90
## 239 2020-03-31 Midlands 123
## 240 2020-04-01 Midlands 140
## 241 2020-04-02 Midlands 142
## 242 2020-04-03 Midlands 124
## 243 2020-04-04 Midlands 151
## 244 2020-04-05 Midlands 164
## 245 2020-04-06 Midlands 140
## 246 2020-04-07 Midlands 123
## 247 2020-04-08 Midlands 186
## 248 2020-04-09 Midlands 139
## 249 2020-04-10 Midlands 127
## 250 2020-04-11 Midlands 142
## 251 2020-04-12 Midlands 139
## 252 2020-04-13 Midlands 120
## 253 2020-04-14 Midlands 116
## 254 2020-04-15 Midlands 147
## 255 2020-04-16 Midlands 102
## 256 2020-04-17 Midlands 118
## 257 2020-04-18 Midlands 115
## 258 2020-04-19 Midlands 92
## 259 2020-04-20 Midlands 107
## 260 2020-04-21 Midlands 86
## 261 2020-04-22 Midlands 78
## 262 2020-04-23 Midlands 103
## 263 2020-04-24 Midlands 79
## 264 2020-04-25 Midlands 72
## 265 2020-04-26 Midlands 81
## 266 2020-04-27 Midlands 74
## 267 2020-04-28 Midlands 68
## 268 2020-04-29 Midlands 53
## 269 2020-04-30 Midlands 56
## 270 2020-05-01 Midlands 64
## 271 2020-05-02 Midlands 51
## 272 2020-05-03 Midlands 52
## 273 2020-05-04 Midlands 61
## 274 2020-05-05 Midlands 58
## 275 2020-05-06 Midlands 59
## 276 2020-05-07 Midlands 48
## 277 2020-05-08 Midlands 34
## 278 2020-05-09 Midlands 37
## 279 2020-05-10 Midlands 42
## 280 2020-05-11 Midlands 33
## 281 2020-05-12 Midlands 45
## 282 2020-05-13 Midlands 39
## 283 2020-05-14 Midlands 37
## 284 2020-05-15 Midlands 40
## 285 2020-05-16 Midlands 34
## 286 2020-05-17 Midlands 31
## 287 2020-05-18 Midlands 34
## 288 2020-05-19 Midlands 34
## 289 2020-05-20 Midlands 36
## 290 2020-05-21 Midlands 32
## 291 2020-05-22 Midlands 27
## 292 2020-05-23 Midlands 34
## 293 2020-05-24 Midlands 19
## 294 2020-05-25 Midlands 26
## 295 2020-05-26 Midlands 33
## 296 2020-05-27 Midlands 29
## 297 2020-05-28 Midlands 27
## 298 2020-05-29 Midlands 20
## 299 2020-05-30 Midlands 20
## 300 2020-05-31 Midlands 21
## 301 2020-06-01 Midlands 20
## 302 2020-06-02 Midlands 21
## 303 2020-06-03 Midlands 23
## 304 2020-06-04 Midlands 15
## 305 2020-06-05 Midlands 21
## 306 2020-06-06 Midlands 19
## 307 2020-06-07 Midlands 14
## 308 2020-06-08 Midlands 15
## 309 2020-06-09 Midlands 17
## 310 2020-06-10 Midlands 14
## 311 2020-06-11 Midlands 12
## 312 2020-06-12 Midlands 2
## 313 2020-03-01 North East and Yorkshire 0
## 314 2020-03-02 North East and Yorkshire 0
## 315 2020-03-03 North East and Yorkshire 0
## 316 2020-03-04 North East and Yorkshire 0
## 317 2020-03-05 North East and Yorkshire 0
## 318 2020-03-06 North East and Yorkshire 0
## 319 2020-03-07 North East and Yorkshire 0
## 320 2020-03-08 North East and Yorkshire 0
## 321 2020-03-09 North East and Yorkshire 0
## 322 2020-03-10 North East and Yorkshire 0
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## 324 2020-03-12 North East and Yorkshire 0
## 325 2020-03-13 North East and Yorkshire 0
## 326 2020-03-14 North East and Yorkshire 0
## 327 2020-03-15 North East and Yorkshire 2
## 328 2020-03-16 North East and Yorkshire 3
## 329 2020-03-17 North East and Yorkshire 1
## 330 2020-03-18 North East and Yorkshire 2
## 331 2020-03-19 North East and Yorkshire 6
## 332 2020-03-20 North East and Yorkshire 5
## 333 2020-03-21 North East and Yorkshire 6
## 334 2020-03-22 North East and Yorkshire 7
## 335 2020-03-23 North East and Yorkshire 9
## 336 2020-03-24 North East and Yorkshire 8
## 337 2020-03-25 North East and Yorkshire 18
## 338 2020-03-26 North East and Yorkshire 21
## 339 2020-03-27 North East and Yorkshire 28
## 340 2020-03-28 North East and Yorkshire 35
## 341 2020-03-29 North East and Yorkshire 38
## 342 2020-03-30 North East and Yorkshire 64
## 343 2020-03-31 North East and Yorkshire 60
## 344 2020-04-01 North East and Yorkshire 67
## 345 2020-04-02 North East and Yorkshire 74
## 346 2020-04-03 North East and Yorkshire 100
## 347 2020-04-04 North East and Yorkshire 105
## 348 2020-04-05 North East and Yorkshire 92
## 349 2020-04-06 North East and Yorkshire 96
## 350 2020-04-07 North East and Yorkshire 102
## 351 2020-04-08 North East and Yorkshire 107
## 352 2020-04-09 North East and Yorkshire 111
## 353 2020-04-10 North East and Yorkshire 117
## 354 2020-04-11 North East and Yorkshire 98
## 355 2020-04-12 North East and Yorkshire 84
## 356 2020-04-13 North East and Yorkshire 94
## 357 2020-04-14 North East and Yorkshire 107
## 358 2020-04-15 North East and Yorkshire 96
## 359 2020-04-16 North East and Yorkshire 103
## 360 2020-04-17 North East and Yorkshire 88
## 361 2020-04-18 North East and Yorkshire 95
## 362 2020-04-19 North East and Yorkshire 88
## 363 2020-04-20 North East and Yorkshire 100
## 364 2020-04-21 North East and Yorkshire 76
## 365 2020-04-22 North East and Yorkshire 84
## 366 2020-04-23 North East and Yorkshire 63
## 367 2020-04-24 North East and Yorkshire 72
## 368 2020-04-25 North East and Yorkshire 69
## 369 2020-04-26 North East and Yorkshire 65
## 370 2020-04-27 North East and Yorkshire 65
## 371 2020-04-28 North East and Yorkshire 57
## 372 2020-04-29 North East and Yorkshire 69
## 373 2020-04-30 North East and Yorkshire 57
## 374 2020-05-01 North East and Yorkshire 64
## 375 2020-05-02 North East and Yorkshire 48
## 376 2020-05-03 North East and Yorkshire 40
## 377 2020-05-04 North East and Yorkshire 49
## 378 2020-05-05 North East and Yorkshire 40
## 379 2020-05-06 North East and Yorkshire 50
## 380 2020-05-07 North East and Yorkshire 45
## 381 2020-05-08 North East and Yorkshire 42
## 382 2020-05-09 North East and Yorkshire 44
## 383 2020-05-10 North East and Yorkshire 40
## 384 2020-05-11 North East and Yorkshire 29
## 385 2020-05-12 North East and Yorkshire 27
## 386 2020-05-13 North East and Yorkshire 28
## 387 2020-05-14 North East and Yorkshire 30
## 388 2020-05-15 North East and Yorkshire 32
## 389 2020-05-16 North East and Yorkshire 35
## 390 2020-05-17 North East and Yorkshire 26
## 391 2020-05-18 North East and Yorkshire 29
## 392 2020-05-19 North East and Yorkshire 27
## 393 2020-05-20 North East and Yorkshire 21
## 394 2020-05-21 North East and Yorkshire 33
## 395 2020-05-22 North East and Yorkshire 22
## 396 2020-05-23 North East and Yorkshire 18
## 397 2020-05-24 North East and Yorkshire 25
## 398 2020-05-25 North East and Yorkshire 21
## 399 2020-05-26 North East and Yorkshire 21
## 400 2020-05-27 North East and Yorkshire 21
## 401 2020-05-28 North East and Yorkshire 19
## 402 2020-05-29 North East and Yorkshire 24
## 403 2020-05-30 North East and Yorkshire 20
## 404 2020-05-31 North East and Yorkshire 19
## 405 2020-06-01 North East and Yorkshire 16
## 406 2020-06-02 North East and Yorkshire 22
## 407 2020-06-03 North East and Yorkshire 22
## 408 2020-06-04 North East and Yorkshire 17
## 409 2020-06-05 North East and Yorkshire 17
## 410 2020-06-06 North East and Yorkshire 20
## 411 2020-06-07 North East and Yorkshire 13
## 412 2020-06-08 North East and Yorkshire 11
## 413 2020-06-09 North East and Yorkshire 11
## 414 2020-06-10 North East and Yorkshire 15
## 415 2020-06-11 North East and Yorkshire 4
## 416 2020-06-12 North East and Yorkshire 5
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## 425 2020-03-09 North West 0
## 426 2020-03-10 North West 0
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## 429 2020-03-13 North West 3
## 430 2020-03-14 North West 1
## 431 2020-03-15 North West 4
## 432 2020-03-16 North West 2
## 433 2020-03-17 North West 4
## 434 2020-03-18 North West 6
## 435 2020-03-19 North West 7
## 436 2020-03-20 North West 10
## 437 2020-03-21 North West 11
## 438 2020-03-22 North West 13
## 439 2020-03-23 North West 16
## 440 2020-03-24 North West 21
## 441 2020-03-25 North West 21
## 442 2020-03-26 North West 29
## 443 2020-03-27 North West 35
## 444 2020-03-28 North West 28
## 445 2020-03-29 North West 46
## 446 2020-03-30 North West 67
## 447 2020-03-31 North West 52
## 448 2020-04-01 North West 86
## 449 2020-04-02 North West 96
## 450 2020-04-03 North West 95
## 451 2020-04-04 North West 98
## 452 2020-04-05 North West 102
## 453 2020-04-06 North West 100
## 454 2020-04-07 North West 134
## 455 2020-04-08 North West 127
## 456 2020-04-09 North West 119
## 457 2020-04-10 North West 117
## 458 2020-04-11 North West 138
## 459 2020-04-12 North West 126
## 460 2020-04-13 North West 129
## 461 2020-04-14 North West 131
## 462 2020-04-15 North West 114
## 463 2020-04-16 North West 134
## 464 2020-04-17 North West 98
## 465 2020-04-18 North West 113
## 466 2020-04-19 North West 71
## 467 2020-04-20 North West 83
## 468 2020-04-21 North West 76
## 469 2020-04-22 North West 86
## 470 2020-04-23 North West 85
## 471 2020-04-24 North West 66
## 472 2020-04-25 North West 65
## 473 2020-04-26 North West 55
## 474 2020-04-27 North West 54
## 475 2020-04-28 North West 57
## 476 2020-04-29 North West 62
## 477 2020-04-30 North West 59
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## 479 2020-05-02 North West 56
## 480 2020-05-03 North West 55
## 481 2020-05-04 North West 48
## 482 2020-05-05 North West 48
## 483 2020-05-06 North West 44
## 484 2020-05-07 North West 49
## 485 2020-05-08 North West 42
## 486 2020-05-09 North West 30
## 487 2020-05-10 North West 41
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## 489 2020-05-12 North West 38
## 490 2020-05-13 North West 25
## 491 2020-05-14 North West 26
## 492 2020-05-15 North West 33
## 493 2020-05-16 North West 32
## 494 2020-05-17 North West 24
## 495 2020-05-18 North West 31
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## 510 2020-06-02 North West 27
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## 514 2020-06-06 North West 23
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## 532 2020-03-12 South East 0
## 533 2020-03-13 South East 1
## 534 2020-03-14 South East 1
## 535 2020-03-15 South East 5
## 536 2020-03-16 South East 8
## 537 2020-03-17 South East 7
## 538 2020-03-18 South East 10
## 539 2020-03-19 South East 9
## 540 2020-03-20 South East 14
## 541 2020-03-21 South East 7
## 542 2020-03-22 South East 25
## 543 2020-03-23 South East 20
## 544 2020-03-24 South East 22
## 545 2020-03-25 South East 29
## 546 2020-03-26 South East 34
## 547 2020-03-27 South East 34
## 548 2020-03-28 South East 36
## 549 2020-03-29 South East 54
## 550 2020-03-30 South East 58
## 551 2020-03-31 South East 65
## 552 2020-04-01 South East 66
## 553 2020-04-02 South East 55
## 554 2020-04-03 South East 72
## 555 2020-04-04 South East 80
## 556 2020-04-05 South East 82
## 557 2020-04-06 South East 88
## 558 2020-04-07 South East 100
## 559 2020-04-08 South East 83
## 560 2020-04-09 South East 104
## 561 2020-04-10 South East 88
## 562 2020-04-11 South East 88
## 563 2020-04-12 South East 88
## 564 2020-04-13 South East 84
## 565 2020-04-14 South East 65
## 566 2020-04-15 South East 72
## 567 2020-04-16 South East 56
## 568 2020-04-17 South East 86
## 569 2020-04-18 South East 57
## 570 2020-04-19 South East 70
## 571 2020-04-20 South East 86
## 572 2020-04-21 South East 50
## 573 2020-04-22 South East 54
## 574 2020-04-23 South East 57
## 575 2020-04-24 South East 64
## 576 2020-04-25 South East 51
## 577 2020-04-26 South East 51
## 578 2020-04-27 South East 40
## 579 2020-04-28 South East 40
## 580 2020-04-29 South East 47
## 581 2020-04-30 South East 29
## 582 2020-05-01 South East 37
## 583 2020-05-02 South East 36
## 584 2020-05-03 South East 17
## 585 2020-05-04 South East 35
## 586 2020-05-05 South East 29
## 587 2020-05-06 South East 25
## 588 2020-05-07 South East 27
## 589 2020-05-08 South East 26
## 590 2020-05-09 South East 28
## 591 2020-05-10 South East 19
## 592 2020-05-11 South East 25
## 593 2020-05-12 South East 27
## 594 2020-05-13 South East 18
## 595 2020-05-14 South East 32
## 596 2020-05-15 South East 24
## 597 2020-05-16 South East 22
## 598 2020-05-17 South East 18
## 599 2020-05-18 South East 22
## 600 2020-05-19 South East 12
## 601 2020-05-20 South East 22
## 602 2020-05-21 South East 14
## 603 2020-05-22 South East 17
## 604 2020-05-23 South East 21
## 605 2020-05-24 South East 16
## 606 2020-05-25 South East 13
## 607 2020-05-26 South East 19
## 608 2020-05-27 South East 17
## 609 2020-05-28 South East 12
## 610 2020-05-29 South East 18
## 611 2020-05-30 South East 8
## 612 2020-05-31 South East 10
## 613 2020-06-01 South East 11
## 614 2020-06-02 South East 12
## 615 2020-06-03 South East 17
## 616 2020-06-04 South East 11
## 617 2020-06-05 South East 9
## 618 2020-06-06 South East 9
## 619 2020-06-07 South East 11
## 620 2020-06-08 South East 5
## 621 2020-06-09 South East 9
## 622 2020-06-10 South East 8
## 623 2020-06-11 South East 3
## 624 2020-06-12 South East 1
## 625 2020-03-01 South West 0
## 626 2020-03-02 South West 0
## 627 2020-03-03 South West 0
## 628 2020-03-04 South West 0
## 629 2020-03-05 South West 0
## 630 2020-03-06 South West 0
## 631 2020-03-07 South West 0
## 632 2020-03-08 South West 0
## 633 2020-03-09 South West 0
## 634 2020-03-10 South West 0
## 635 2020-03-11 South West 1
## 636 2020-03-12 South West 0
## 637 2020-03-13 South West 0
## 638 2020-03-14 South West 1
## 639 2020-03-15 South West 0
## 640 2020-03-16 South West 0
## 641 2020-03-17 South West 2
## 642 2020-03-18 South West 2
## 643 2020-03-19 South West 5
## 644 2020-03-20 South West 3
## 645 2020-03-21 South West 6
## 646 2020-03-22 South West 9
## 647 2020-03-23 South West 9
## 648 2020-03-24 South West 7
## 649 2020-03-25 South West 9
## 650 2020-03-26 South West 11
## 651 2020-03-27 South West 13
## 652 2020-03-28 South West 21
## 653 2020-03-29 South West 18
## 654 2020-03-30 South West 23
## 655 2020-03-31 South West 23
## 656 2020-04-01 South West 22
## 657 2020-04-02 South West 23
## 658 2020-04-03 South West 30
## 659 2020-04-04 South West 42
## 660 2020-04-05 South West 32
## 661 2020-04-06 South West 34
## 662 2020-04-07 South West 39
## 663 2020-04-08 South West 47
## 664 2020-04-09 South West 24
## 665 2020-04-10 South West 46
## 666 2020-04-11 South West 43
## 667 2020-04-12 South West 23
## 668 2020-04-13 South West 27
## 669 2020-04-14 South West 24
## 670 2020-04-15 South West 32
## 671 2020-04-16 South West 29
## 672 2020-04-17 South West 33
## 673 2020-04-18 South West 25
## 674 2020-04-19 South West 31
## 675 2020-04-20 South West 26
## 676 2020-04-21 South West 26
## 677 2020-04-22 South West 23
## 678 2020-04-23 South West 17
## 679 2020-04-24 South West 19
## 680 2020-04-25 South West 15
## 681 2020-04-26 South West 27
## 682 2020-04-27 South West 13
## 683 2020-04-28 South West 17
## 684 2020-04-29 South West 15
## 685 2020-04-30 South West 26
## 686 2020-05-01 South West 6
## 687 2020-05-02 South West 7
## 688 2020-05-03 South West 10
## 689 2020-05-04 South West 17
## 690 2020-05-05 South West 14
## 691 2020-05-06 South West 19
## 692 2020-05-07 South West 16
## 693 2020-05-08 South West 6
## 694 2020-05-09 South West 11
## 695 2020-05-10 South West 5
## 696 2020-05-11 South West 8
## 697 2020-05-12 South West 7
## 698 2020-05-13 South West 7
## 699 2020-05-14 South West 6
## 700 2020-05-15 South West 4
## 701 2020-05-16 South West 4
## 702 2020-05-17 South West 6
## 703 2020-05-18 South West 4
## 704 2020-05-19 South West 6
## 705 2020-05-20 South West 1
## 706 2020-05-21 South West 9
## 707 2020-05-22 South West 6
## 708 2020-05-23 South West 6
## 709 2020-05-24 South West 3
## 710 2020-05-25 South West 8
## 711 2020-05-26 South West 11
## 712 2020-05-27 South West 5
## 713 2020-05-28 South West 9
## 714 2020-05-29 South West 4
## 715 2020-05-30 South West 3
## 716 2020-05-31 South West 2
## 717 2020-06-01 South West 6
## 718 2020-06-02 South West 2
## 719 2020-06-03 South West 5
## 720 2020-06-04 South West 2
## 721 2020-06-05 South West 2
## 722 2020-06-06 South West 1
## 723 2020-06-07 South West 3
## 724 2020-06-08 South West 3
## 725 2020-06-09 South West 0
## 726 2020-06-10 South West 0
## 727 2020-06-11 South West 2
## 728 2020-06-12 South West 21.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 11 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.5577 -2.1654 -0.4145 2.1843 4.4965
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.033e+00 5.204e-02 96.71 <2e-16 ***
## note_lag 1.093e-05 5.095e-07 21.46 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 9.165198)
##
## Null deviance: 4560.68 on 42 degrees of freedom
## Residual deviance: 387.83 on 41 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 153.346419 1.000011
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 138.33132 169.637030
## note_lag 1.00001 1.000012
Rsq(lag_mod)
## [1] 0.9149634
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.4.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.5.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.2
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0